Information Theory
The course introduces the main concepts in information theory and algorithmic information theory.
Courses
The program in Data Science and Scientific Computing is organized in three curricula:
- Artificial Intelligence and Machine Learning
- Data Science and Applications
- Computational Science and Engineering
Each curriculum is further organized in several study plans, each corresponding to a specific application area, proposing a choice of specialization courses.
Check out the Study Plan page for more information.
Advanced Algorithms for Data Science
Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.
NOTE: The equivalent name of this course is "Algorithms for Massive Data".
Advanced Numerical Analysis
Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.
Advanced Programming and Algorithmic Design
Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development.
Advanced Topics In Machine Learning
This course will be available from the Academic Year 2022/23.
Bayesian Statistics
Objective: introducing techniques of analysis and statistical Bayesian inference.
Bioinformatics and Genomic Data Analytics
Genome sequencing technologies have revolutionised our approach to study biological systems. From healthy tissues to diseases, many questions are approached through the collection of far larger sequencing datasets, and some argue that all modern biology is computational biology. Sequencing technologies can measure molecular states at various degrees of resolution and noise, and complex bioinformatics/ machine learning pipelines are required to extract complex patterns that characterise from basic cellular processes to disease progression.
Computational Physics Laboratory
Objective: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
Computational Quantum Chemistry
Objective: providing knowledge of the most important theoretical formalism used in quantum chemistry, and of the main computational methods, numerical algorithms, and software tools in the field of quantum chemistry.
Computer Vision and Pattern Recognition
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Control Theory
Objective: providing advanced notions of the theory of dynamical systems both in continuous and in discrete time. Introduce to modern techniques for the design of complex control systems with particular reference to application contexts of engineering interest in the industrial field.
Cyber-Physical Systems
Objective: introduces the students to the design and analysis of Cyber-Physical Systems, we will see how to model such systems, how to specify and monitor their behaviors using formal languages as temporal logics, and how to use monitoring techniques for different applications as parameter synthesis and falsification test
Data Management for Big Data
Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Earth Sciences Analytics
The course will introduce the student to data sources, problems, main concepts and models in Earth Science, with a focus on analytics using these data.
Fluid Dynamics
Objective: introducing the physical and mathematical principles of fluid dynamics.
Formation of Cosmological Large-Scale Structures
Objective: providing a thorough and updated knowledge of cosmology issues related to the study of the formation of galaxies, clusters of galaxies and the structure of large-scale Universe in current cosmological models, through analytical, numerical, and statistical techniques for the evolution of disturbances in linear and nonlinear regime.
Foundations of High Performance Computing
Objective: introducing students to modern architectures for high performance computing. Students will learn how to properly test such architectures (computing power, bandwidth, latency, energy efficiency). Leveraging these skills, students will be introduced to the parallel programming based on MPI protocols (Message Passing Interface) and multi-threading with OpenMP.
Galaxy Astrophysics
Objective: providing an overview, in the context of modern astronomy, to the various cosmic objects and give the basic principles necessary for the determination of their fundamental physical quantities.
Esse3 Course Page A.Y 2019/2020: https://esse3.units.it/Guide/PaginaADErogata.do?cod_lingua=eng&ad_er_id=...
Geophysics Analytics
The course will introduce the student to the main problems, data sources, and models in Geophysics.
Global and Multi-Objective Optimization
This course will be available from the Academic Year 2022/23.
Health Data Analytics
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
Information Retrieval and Data Visualization
Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization.
Introduction to Cosmology
Objective: introducing to the foundations of modern cosmological theories.
Introduction to Machine Learning
Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.
Introduction to Quantum Information Theory
The course introduces the main concepts and effects of quantum correlation on information theory, computation and machine learning.
Introduction to Quantum Mechanics and Quantum Computing
Introducing to main concepts in quantum mechanics and quantum computing
Management of Health Data
Objective: providing expertise for the management of clinical and biomedical data from computerized medical records, through the methods of health information technology and of process modeling.
Molecular Simulation
Objective: introducing the computational techniques used in molecular modeling and simulation, and illustrating how these techniques can be employed to describe and/ or predict chemical, physical and biological phenomena.
Natural Language Processing
Objective: introduce modern computational techniques and machine learning techniques for the analysis of natural language.
Network Science
Objective: You will learn how to organize, transform, analyse and visualize data, with a focus on the relational data model, and a detour to semistructured data. You will learn the fundamentals of data science using R environment.
Numerical Analysis
Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.
Numerical Methods in Quantum Mechanics
Objective: providing an introduction to numerical methods and techniques for the numerical solution of quantum mechanical problems, especially in atomic physics and condensed matter, with a practical approach.
Open Data Management and the Cloud
Objective: providing students with practical information on how to design data models and data structures, to manage metadata to optimize access and research, and to become familiar with interoperability standards. The course will focus on the concept of open data, with efficiency for big data projects, and the concept of cloud as an infrastructure for data management and their processes.
Physics and Modelling of Turbulent Flows
Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used to solve such models.
Probabilistic Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.
Radiative Processes in Astrophysics
Introduce the student to the physics of the inner stars and on radiative processes fundamental in astrophysics.
Reinforcement Learning
The main ideas and methodologies of Reinforcement Learning will be introduced.
Simulation of Multibody Systems
Objective: providing the ability to understand the functioning and the internal structure of a molecular dynamics program. Being able to write code for molecular dynamics simulations and to analyze the output.
Software Development Methods
Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.
Statistical Analysis of Networks
Objective: presenting statistical analysis techniques for social networks and other social and economic networks.
NOTE: The equivalent name of this course is "Social Network Analysis".
Statistical Mechanics
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
Statistical Methods for Data Science
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.
Stochastic Modelling and Simulation
Objective: introducing students to the fundamentals and practice of stochastic modeling, simulation of stochastic models and inference of parameters starting from observations, with a focus on scalability for large models.